Determining intensity of a biological response to a presentation

ABSTRACT

Example methods, apparatus/systems and articles of manufacture for determining intensity of a biological response to a presentation are disclosed. An example method includes accessing galvanic skin response (GSR) data obtained from a subject while exposed to a presentation. The GSR data includes a plurality of trough-to-peak instances. The example method includes generating a GSR intensity profile by assigning trough-to-peak scores to corresponding ones of the trough-to-peak instances, defining a plurality of time windows, and assigning window scores to corresponding ones of the time windows based on the trough-to-peaks scores of the trough-to-peak instances occurring within the corresponding time windows. The example method also includes determining an effectiveness of the presentation based on the window scores of the GSR intensity profile.

RELATED APPLICATION

This patent arises from a continuation of U.S. application Ser. No.15/089,955 (now U.S. Pat. No. 10,314,510), titled “DETERMINING INTENSITYOF A BIOLOGICAL RESPONSE TO A PRESENTATION,” filed Apr. 4, 2016, whichclaims the benefit under 35 U.S.C. § 119(e) to U.S. ProvisionalApplication No. 62/272,932, titled “DETERMINING INTENSITY OF ABIOLOGICAL RESPONSE TO A PRESENTATION,” filed Dec. 30, 2015, both ofwhich are incorporated herein by this reference in their entireties.

FIELD OF THE DISCLOSURE

This disclosure relates generally to biological responses and, moreparticularly, to determining intensity of a biological response to apresentation.

BACKGROUND

Galvanic skin response is a type of a biological response indicative ofarousal.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an example intensity measurement system including anexample monitoring station constructed in accordance with the teachingsof this disclosure.

FIG. 2 is an example graph of galvanic skin response (GSR) data obtainedfrom an example baseline test using the example intensity measurementsystem of FIG. 1.

FIG. 3 is an example histogram of example trough-to-peak instances ofthe example GSR data from the example baseline test of FIG. 2 generatedusing the example intensity measurement system of FIG. 1.

FIG. 4 includes two example graphs generated using the example intensitymeasurement system of FIG. 1. The first graph illustrates example GSRdata obtained by the example intensity measurement system of FIG. 1 andthe second graph illustrates example scores assigned to exampletrough-to-peak instances occurring in the example GSR data of the firstgraph.

FIG. 5 includes two example graphs generated using the example intensitymeasurement system of FIG. 1. The first graph is an enlarged view of thefirst example graph from FIG. 4 and illustrates example windows and thesecond graph illustrates an example intensity profile for thepresentation.

FIG. 6 is an example graph showing a normalized view of the exampleintensity profile of FIG. 5 generated using the example intensitymeasurement system of FIG. 1.

FIG. 7 is an example graph showing an example aggregated intensityprofile generated using the example intensity measurement system of FIG.1.

FIGS. 8A and 8B are flowcharts representative of example machineexecutable instructions, which may be executed to implement the exampleintensity measurement system FIG. 1.

FIG. 9 is a block diagram of an example processor system structured toexecute example machine readable instructions represented by FIGS. 8Aand 8B to implement the example intensity measurement system of FIG. 1.

Certain examples are shown in the above-identified figures and describedin detail below. In describing these examples, like or identicalreference numbers are used to identify the same or similar elements. Thefigures are not necessarily to scale and certain features and certainviews of the figures may be shown exaggerated in scale or in schematicfor clarity and/or conciseness. Additionally, several examples have beendescribed throughout this specification. Any features from any examplemay be included with, a replacement for, or otherwise combined withother features from other examples.

DETAILED DESCRIPTION

Disclosed herein are example methods, apparatus/systems and articles ofmanufacture that may be implemented to determine the intensity of anaudience's biological response to a presentation (e.g., a sensorystimulus, media (e.g., content and/or advertisement), entertainment,etc.). The audience may include one or more subjects (e.g., aparticipant, a user, a panelist, a patient, a member of an audience,etc.) and the presentation may be any live or recorded, audio, visual oraudio-visual material, for example. Disclosed example methods,apparatus/systems and articles of manufacture implement technique(s)that obtain biological response data, such as, for example, galvanicskin response (GSR) data, from a subject while the subject is exposed toa presentation and generate an intensity profile of the biologicalresponse to the presentation. As used herein, “intensity” means thestrength of a measured biometric signal in response to a stimulus. Anexample technique disclosed herein includes identifying trough-to-peakinstances (e.g., spikes, events) in the response data, ranking thetrough-to-peak instances, and scoring the trough-to-peak instances basedon the ranks. The score (e.g., a trough-to-peak score) to which atrough-to-peak instance is assigned depends on the magnitude of theincrease in the response signal between the corresponding peak andtrough. For example, a trough-to-peak instance may be scored based on ascale from 1 to 4. In this scale, 1 corresponds to a relatively lowincrease in the biometric signal and a 4 corresponds to a relativelylarge increase in the biometric signal. In other examples, other scoreranges (e.g., scales, levels) having more or fewer discrete score valuesmay be used (e.g., 0-3, 0-10, 5-30, etc.). In some examples, using arange having more score values (e.g., more discrete score values)enhances the ability to identify more subtle differences between thetrough-to-peak values.

In some examples, score values are determined based on statisticalboundaries. In some examples, the statistical boundaries are determinedfrom a baseline test (e.g., a baseline video segment presented to thesubject before the target presentation). For example, response data froma baseline test may be obtained, the trough-to-peak instances may beidentified and measured, and the range (e.g., bins) of thetrough-to-peak values may be used to establish boundaries between thedifferent score value levels. The range of trough-to-peak values may bedivided into percentiles (e.g., bins, increments, levels, etc.). Inother words, percentiles of the trough-to-peak value range may be usedto set boundaries for score levels. For example, the trough-to-peakvalue range may be split into quartiles. The first quartile maycorrespond to a range of lower trough-to-peak values, the secondquartile may correspond to a range of lower to mid-level trough-to-peakvalues, the third quartile may correspond to a range of mid-level tohigh trough-to-peak values, and the fourth quartile may correspond tohigher trough-to-peak values. Then, the trough-to-peak instances of theresponse data for the target presentation can be scored based on thescore values of the respective quartiles.

In some examples, the scores (e.g., first scores, the trough-to-peakscores) are assigned to and/or associated with the troughs of therespective trough-to-peak instances (e.g., aligned with the time of thetrough). In some examples, the scores of the trough-to-peak instancesare assigned to windows of time to generate an intensity profile. Insome examples, the windows have fixed lengths and are based on a slidingscale, which is disclosed in further detail herein. The windows may beassigned scores (e.g., second scores, window scores) based on the scoresof the trough-to-peak instances occurring with the respective windows.In some examples, the windows are assigned scores corresponding to thehighest scores of the trough-to-peak instance(s) occurring within therespective windows. An intensity profile may be generated based on thewindow scores. The intensity profile more accurately reflects thechanges in the level of the response occurring during the presentationthan the raw response data. In some examples, the intensity profilesfrom multiple subjects may be averaged and/or normalized by a maximumscore value to yield a result in a desired range (e.g., 0-1). Theintensity profiles can be used to identify elements (e.g., scenes,events, etc.) in the presentation that cause or elicit high and/or lowlevels of response and, thus, are linked to arousal, engagement,interest, etc. with the presentation.

Example methods, apparatus/systems and articles of manufacture disclosedherein can determine a measure of overall and/or moment-to-momentprofile of intensity of the biological response. The intensity of theaudiences' response to a presentation may indicate the level ofengagement, focus, interest, etc. in the presentation. Therefore, themeasure of intensity can be used to estimate the level to which anaudience will be engaged by, like, dislike, etc. a same or similarpresentation. In other words, by accurately measuring the intensity ofthe audience's response to a presentation, example methods,apparatus/systems and articles of manufacture disclosed herein can beused to better predict the audience's response(s) to anotherpresentation. Additionally or alternatively, the example intensityprofile may then be used to identify elements of the presentation thatcontribute to high levels of intensity and, thus, the effectiveness andsuccess of the presentation.

Example methods, apparatus/systems and articles of manufacture disclosedherein can be used by directors, entertainment specialists, politicians,advertisers, media creators, marketers, distributors, etc. to accuratelyevaluate their presentation(s) prior to distribution/publication, forexample, by objectively determining an audience's response to thepresentation. Being able to estimate the overall impact of a givenstimulus is important to, for example, promoters in identifying a targetaudience, corporate sponsors and advertisers for advertising purposes,clinicians for educating patients, teachers for educating and/orinspiring students, politicians for garnering votes, etc. Examplemethods, apparatus/systems and articles of manufacture disclosed hereinmay be used to determine which, if any, demographic group may find aparticular presentation or portion of a presentation to be arousing tohelp determine its impact and make appropriate adjustments prior togeneral release. Additionally or alternatively, the measure of intensityof the sample population audience can be used to estimate the level towhich the population, as a whole, may be aroused by (e.g., like,dislike, be indifferent to, attention captured by) the samepresentation.

Example techniques disclosed herein are described in connection withgalvanic skin response (GSR) data. Additionally or alternatively, theexamples disclosed herein may likewise be applied to other types ofbiological responses such as heart rate, respiration rate, respirationstate, body motion, eye tracking, functional magnetic resonance imaging(fMRI), electroencephalography (EEG), electrocardiograms (EKG),pupillary dilation, electrooculography (EOG), facial emotion encoding,reaction time and/or any other biologically based responses.

In general, galvanic skin response (GSR) or electrodermal activity (EDA)is the change in electrical properties occurring on the skin. GSR or EDAmay also be referred to as electrodermal response (EDR), psychogalvanicreflex (PGR), skin conductance response (SCR) and skin conductance level(SCL). The signal can be used to capture the autonomic nerve responsesas a parameter of the sweat gland function. In particular, the skinresistance varies with the state of the sweat glands in the skin.Sweating, which is controlled by the sympathetic nervous system, isindicative of psychological or physiological arousal. Therefore,measuring the conductance of the skin is an accurate measure of asubject's emotional and/or sympathetic responses. GSR can be measuredusing electrodes placed on the skin of the subject (e.g., on the palm,on the finger tips, etc.).

In some examples, the intensity measurements (with or without additionalmeasures, such as synchrony) are used to determine engagement. Theexample intensity measurements may be used, for example, in place of theexample intensity measurements disclosed in U.S. Pat. No. 8,296,172,titled “Methods and System for Determining Audience Response to aSensory Stimulus,” filed Sep. 5, 2007, which is incorporated by thisreference in its entirety.

An example method disclosed herein includes accessing galvanic skinresponse (GSR) data obtained from a subject while exposed to apresentation. The GSR data includes a plurality of trough-to-peakinstances. The example method includes generating, by executing aninstruction with a processor, a GSR intensity profile by assigningtrough-to-peak scores to corresponding ones of the trough-to-peakinstances, defining a plurality of time windows, where each of the timewindows commences a first time period after a preceding time window andeach of the time windows has a duration of a second time period, wherethe second time period is greater than the first time period, andassigning window scores to corresponding ones of the time windows basedon the trough-to-peak scores of the trough-to-peak instances occurringwithin the corresponding time windows to generate the GSR intensityprofile. The example method also includes determining, by executing aninstruction with the processor, an effectiveness of the presentationbased on the window scores of the GSR intensity profile.

In some examples, the trough-to-peak instances include correspondingtroughs and peaks, and the processor generates the GSR intensity profileby assigning the trough-to-peak scores to the troughs of thecorresponding trough-to-peak instances. In some examples, the methodincludes identifying the highest trough-to-peak score within thecorresponding time windows, and the assigning of the window scores tothe time windows includes selecting the highest trough-to-peak scoreoccurring within the corresponding time windows as the correspondingwindow scores. In some examples, the time windows have correspondingstart times and end times, and the assigning of the window scores to thetime windows includes assigning the window scores to the start times ofthe corresponding time windows. In some examples, the GSR intensityprofile is a graph of window scores over time.

In some examples, the subject is a first subject, and the example methodincludes averaging the GSR intensity profile of the first subject with aGSR intensity profile of a second subject to create an aggregated GSRintensity profile, normalizing the aggregated GSR intensity profile anddetermining the effectiveness of the presentation based on thenormalized aggregated GSR intensity profile. In some examples, themethod includes identifying trough-to-peak instances in baseline GSRdata obtained from the subject during a baseline test, dividing theidentified trough-to-peak instances into ranges and respectivelyassigning baseline values to the ranges, where the trough-to-peak scoresof the trough-to-peak instances are based on corresponding ones of thebaseline values. In some examples, a first time window of the pluralityof time windows and a second time window of the plurality of timewindows overlap in time.

In some examples, the method includes identifying, by executing aninstruction with the processor, an element of the presentationcorresponding to a level of GSR satisfying a threshold. In some suchexamples, the method further includes modifying the identified elementof the presentation to no longer satisfy the threshold.

An example apparatus disclosed herein includes a presentation device topresent a stimulus material to a subject, a galvanic skin response (GSR)sensor to gather GSR data from the subject while the subject is exposedto the stimulus material, a trough-to-peak scorer to assigntrough-to-peak scores to corresponding trough-to-peak instances in theGSR data and a window generator to define a plurality of time windows.Each of the time windows commences a first time period after a precedingtime window and each of the time windows has a duration of a second timeperiod, where the second time period is greater than the first timeperiod. The example apparatus also concludes a window scorer to assignwindow scores to corresponding ones of the time windows based on thetrough-to-peaks scores of the trough-to-peak instances occurring withinthe corresponding windows, an intensity profile generator to generate aGSR intensity profile based on the window scores and an effectivenessdeterminer to determine an effectiveness of the presentation based onthe window scores of the GSR intensity profile.

In some examples, the trough-to-peak instances include correspondingtroughs and peaks, and the window scorer is to assign the trough-to-peakscores to the troughs of the corresponding trough-to-peak instances. Insome examples, the window scorer is to assign the window scores to thecorresponding time windows by selecting the highest trough-to-peak scoreoccurring within the corresponding time windows as the correspondingwindow scores. In some examples, the time windows have correspondingstart times and end times, and the window scorer is to assign the windowscores to the time windows by assigning the window scores to the starttimes of the corresponding time windows. In some examples, the GSRintensity profile is a graph of the window scores over time.

In some examples, the subject is a first subject, and the intensityprofile generator is to average the GSR intensity profile of the firstsubject with a GSR intensity profile of a second subject to create anaggregated GSR intensity profile and normalize the aggregated GSRintensity profile. In such an example, the effectiveness of thepresentation is based on the normalized aggregated GSR intensityprofile.

In some examples, the apparatus includes a trough-to-peak identifier toidentify trough-to-peak instances in baseline GSR data obtained from thesubject during a baseline test and a score value determiner to dividethe identified trough-to-peak instances into ranges and respectivelyassign baseline values to the ranges. In some such examples, thetrough-to-peak scores of the trough-to-peak instances are based oncorresponding ones of the baseline values. In some examples, a firsttime window of the plurality of time windows and a second time window ofthe plurality of time windows overlap in time.

In some examples, the apparatus includes a presentation modifier toidentify an element of the presentation corresponding to a level of GSRsatisfying a threshold. In some such examples, the presentation modifieris to modify the identified element of the presentation to no longersatisfy the threshold.

Disclosed herein is an example tangible machine readable storage mediumhaving instructions that, when executed, cause a machine to at leastassign trough-to-peak scores to corresponding trough-to-peak instancesin galvanic skin response (GSR) data. The GSR data is obtained from asubject while the subject is exposed to a presentation. The exampleinstructions, when executed, cause the machine to define a plurality oftime windows, where each of the time windows commences a first timeperiod after a preceding time window and each of the time windows has aduration of a second time period, where the second time period greaterthan the first time period. The example instructions, when executed,also cause the machine to generate a GSR intensity profile by assigningwindow scores to corresponding ones of the time windows based on thetrough-to-peaks scores of the trough-to-peak instances occurring withinthe corresponding time windows and determine an effectiveness of thepresentation based on the window scores of the GSR intensity profile.

In some examples, the trough-to-peak instances include correspondingtroughs and peaks, and the instructions, when executed, cause themachine to generate the GSR intensity profile by assigning thetrough-to-peak scores to the troughs of the corresponding trough-to-peakinstances. In some examples, the instructions, when executed, furthercause the machine to identify the highest trough-to-peak score withinthe corresponding time windows and assign the window scores to the timewindows by selecting the highest trough-to-peak score occurring withinthe corresponding time windows as the corresponding window scores. Insome examples, the time windows have corresponding start times and endtimes, and the instructions, when executed, cause the machine to assignthe window scores to the time windows by assigning the window scores tothe start times of the corresponding time windows. In some examples, theGSR intensity profile is a graph of the window scores over time.

In some examples, the subject is a first subject, and the instructions,when executed, further cause the machine to average the GSR intensityprofile of the first subject with a GSR intensity profile of a secondsubject to create an aggregated GSR intensity profile, normalize theaggregated GSR intensity profile and determine the effectiveness of thepresentation based on the normalized aggregated GSR intensity profile.In some examples, the instructions, when executed, further cause themachine to identify trough-to-peak instances in baseline GSR dataobtained from the subject during a baseline test, divide the identifiedtrough-to-peak instances into ranges and respectively assign baselinevalues to the ranges, where the trough-to-peak scores of thetrough-to-peak instances are based on the corresponding ones of thebaseline values. In some examples, a first time window of the pluralityof time windows and a second time window of the plurality of timewindows overlap in time.

In some examples, the instructions, when executed, further cause themachine to identify an element of the presentation corresponding a levelof GSR satisfying a threshold. In some such examples, the instructions,when executed, further cause the machine to modify the identifiedelement of the presentation to no longer satisfy the threshold.

FIG. 1 illustrates an example intensity measurement system 100 formeasuring a level of intensity of a biological response of a subject 102responding to a stimulus. The example system 100 of FIG. 1 includes asensor 104 coupled to the subject 102. The sensor 104 of the illustratedexample is a GSR sensor that obtains biometric data (e.g., GSR data)while the subject 102 is exposed to a stimulus (e.g., a presentation,media, content, etc.). The sensor 104 may include one or moreelectrodes. In the illustrated example, the sensor 104 is coupled to ahand of the subject 102. However, in other examples, the sensor 104 maybe attached to other areas of the body and/or may include multiplesensors (e.g., multiple electrodes) attached to other areas of the body.Additionally or alternatively, in some examples other types of sensors(e.g., a camera, an EEG electrode, etc.) may be used to gather biometricdata representative of other types of biological responses, such as, forexample, heart rate, respiration rate, brain waves, pupillary dilation,facial motion, etc.

In the illustrated example, the subject 102 is exposed to a presentationfrom a presentation device 106. In the illustrated example, thepresentation device 106 is implemented as a television (TV), and thepresentation is a television broadcast or downloaded (e.g., streamed)program. In other examples, the presentation 106 may be any live orrecorded, passive or interactive audio, visual, or audio-visualpresentation. The presentation device 106 may include, for example, aTV, a computer monitor, a radio, a smart phone, a tablet, a gamingconsole, a projection system, a speaker, a streaming device, and/or anyother display or presentation device. For example, the presentation maybe a live or recorded viewing of a soccer game on a TV. In anotherexample, the presentation may be a live viewing of the soccer game whilethe subject 102 is at the stadium. In another example, the presentationmay be a picture on a computer screen or in a document (e.g., anewspaper) that the subject 102 is viewing.

While the subject 102 is exposed to the presentation, a monitoringstation 108 receives and records the GSR data obtained by the sensor104. In the illustrated example, the sensor 104 is communicativelycoupled to the monitoring station 108 via a wire. In other examples, thesensor 104 wirelessly communicates the GSR data to the monitoringstation 108. In some examples, information relating to the timing of thepresentation is received by the monitoring station 108. In theillustrated example, the presentation device 106 is communicativelycoupled to the monitoring station 108 via a wire. In other examples, thepresentation device 106 wirelessly communicates with the monitoringstation 108. The monitoring station 108 may be remote from the subject102 and/or the presentation device 106. In other words, the GSR dataobtained by the sensor 104 and/or the presentation information may beobtained offsite and transmitted to the monitoring station 108 in realtime or after the subject 102 has been exposed to the presentation. Insome examples, the presentation is a live viewing of an event (e.g., asoccer game) and the presentation device 106 is a camera recording theevent. In such an example, the collected GSR data is transmitted to themonitoring station 108 with corresponding images (and/or timinginformation) collected by the camera.

In the illustrated example, the monitoring station 108 includes amonitor or display 110, such as a computer monitor. However, in otherexamples, the monitoring station 108 may not include a display. Theexample monitoring station 108 of FIG. 1 includes a database 114, atrough-to-peak (T2P) identifier 116, a T2P value determiner 118, a scorevalue determiner, a T2P scorer 120, a window generator 122, a windowscorer 124, an intensity profile generator 126, an effectivenessdeterminer 128 and a presentation modifier 130, the structures andoperations of which are described in connection with the followingexamples illustrated in FIGS. 2-7.

In some examples, prior to analyzing the GSR data obtained from thesubject 102, baseline GSR data is obtained and analyzed by themonitoring station 108 to determine score values (e.g., baseline values)for use when ranking the GSR data from the target or desiredpresentation. FIG. 2 shows an example GSR graph 200 displaying examplebaseline GSR data 202 obtained from the subject 102 during an examplebaseline presentation. The baseline presentation may be a test or samplepresentation presented to the subject 102. The baseline presentation maybe a shortened version of the target presentation or an entirelydifferent presentation. In some examples, the baseline presentation isintended to elicit or evoke the whole range of a subject's possibleresponses to a stimulus. In other examples, the baseline is used todetermine a subset of subject response levels. The GSR graph 200 may bedisplayed on the monitor 110, for example. The monitoring station 108obtains the GSR data 202 from the subject 102 while the subject 102 isexposed to the baseline test. The GSR data 202 may be recorded in thedatabase 114, for example. The Y-axis of the GSR graph 200 representsthe strength of the signal gathered by the sensor 104, and the X-axis ofthe GSR graph 200 represents time in seconds. In the illustratedexample, the Y-axis of the GSR graph 200 represents the conductance ofthe skin measured by the sensor 104 and shows the strength of the GSRsignal. The Y-axis may be represented, for example, in micro-Siemens(0), which is a unit of electrical conductance. In other examples, theY-axis includes unit-less numbers that identify the relative strength ofa response. In other examples, the GSR data 202 may be measured and/ordisplayed in different parameters or units (e.g., kilo-Ohms) dependingon the type of biometric sensor device.

As illustrated in the GSR graph 200, the GSR data 202 includes aplurality of trough-to-peak instances (e.g., spikes, events, etc.). Arise in the GSR data 202 is often indicative of arousal, which in thiscontext is caused by the presentation. The T2P identifier 116 identifiesthe T2P (trough-to-peak) instances in the GSR data 202, taken from atrough to a subsequent peak. An example T2P instance 204 is circled inthe GSR graph 200. The GSR data 202 may include more or fewer T2Pinstances depending on the subject's response.

The T2P value determiner 118 measures the change in GSR from the troughto the peak of the respective T2P instances. After the T2P valuedeterminer 118 determines the values or levels of rise (e.g., theamplitude changes) in the GSR data 202, the score value determiner 119divides the values into percentiles (e.g., bins) and assigns certainscore values (e.g., baseline values) to each of the percentiles. Forexample, FIG. 3 shows an example histogram 300 of the T2P instancesgathered during an example baseline presentation. The Y-axis of thehistogram 300 represents the number of T2P instances and the X-axis ofthe histogram 300 represents the corresponding GSR values(trough-to-peak values) for the T2P instances. In the illustratedexample, the values of the T2P instances are be divided into rangesrepresented by Quartiles 1, 2, 3 and 4, as shown in FIG. 3. Eachquartile may be assigned a specific score value. For example, Quartile 1may be assigned a score value of 1, Quartile 2 may be assigned a scorevalue of 2, Quartile 3 may be assigned a score value of 3, and Quartile4 may be assigned a score value of 4. Then, when measuring the GSR datafrom the subject 102 during a target presentation, a T2P instance havinga GSR value within Quartile 1 (e.g., 0.00-0.06 μS) may be assigned ascore of 1, a T2P instance having a GSR value within Quartile 2 (e.g.,0.06-0.09 μS) may be assigned a score of 2, a T2P instance having a GSRvalue within Quartile 3 (e.g., 0.09-0.16 μS) may be assigned a score of3, and a T2P instance having a GSR value within Quartile 4 (e.g.,0.16-0.30 μS) may be assigned a score of 4. In other examples, the T2Pvalues may be divided or defined into other percentiles (e.g., thirds,halves, etc.) and/or based on other statistical boundaries. In otherexamples, other score values may be assigned to the percentiles orlevels. For example, Quartile 1 may be assigned a score value of 0,Quartile 2 may be assigned a score value of 1, Quartile 3 may beassigned a score value of 2 and Quartile 4 may be assigned a score valueof 3. The histogram 300 may be displayed on the monitor 110, forexample.

FIG. 4 illustrates an example GSR graph 400 displaying example GSR data402 (e.g., a GSR timeline) obtained while the subject 102 is exposed tothe presentation (e.g., the target presentation). The Y-axis of the GSRgraph 400 represents the strength of the signal gathered by the sensor104, and the X-axis of the GSR graph 400 represents time in seconds. Inthe illustrated example, the Y-axis is the resistance in μS. However, inother examples, the GSR values may be measured in different parameters.As shown in the illustrated example of FIG. 4, the GSR data 402 includesa plurality of T2P instances. The GSR graph 400 may be displayed on themonitor 110, for example. After or while the GSR data 402 is beingobtained (and/or recorded), the T2P identifier 116 identifies the T2Pinstances in the GSR data 402, including, for example, the points intime corresponding to the troughs (i.e., the lowest point of the GSRvalue before an increase) and the peaks (i.e., the highest point in theGSR value before a decrease). The T2P value determiner 118 measures thevalues or levels of increase for the T2P instances (e.g., the change inμS from the trough to the peak).

Once the values or levels of increase are determined for the T2Pinstances, the T2P scorer 120 determines scores for the T2P instancesbased on the values. In other words, the T2P scorer 120 ranks the T2Pinstance based on the level or rise of GSR data 402. In some examples,the scores are based on the score values determined from a baselinetest, such as shown in the histogram 300 of FIG. 3. For example, a firstT2P instance 404 is identified in the GSR data 402. The increase in GSRvalue between the trough and the peak is about 0.05 μS. A GSR increaseof 0.05 μS corresponds to Quartile 1 of the histogram 300 in FIG. 3. Assuch, the first T2P instance 404 is assigned a score of 1. In someexamples, the scores of the T2P instances are plotted in a score graph406, as illustrated in FIG. 4. In the illustrated example, the score ofthe first T2P instance 404 is assigned to or associated with the timecorresponding to the trough of the first T2P instance 404. In someinstances, GSR has a delayed reaction to a stimulus. As such, by scoringthe first T2P instance 404 at the trough, the score is more accuratelyaligned with the element(s) in the presentation that caused or elicitedthe corresponding rise or spike in GSR signal. In other examples, thescore can be assigned to another time or point of the first T2P instance404. The T2P identification, value determination and score determinationmay be repeated for each of the T2P instances in the GSR data 402. Theexample score graph 406 illustrates all of the scores of the T2Pinstances occurring in the GSR data 402. As illustrated, the scores ofthe respective T2P instances are assigned to or associated with the timeof the troughs (e.g., the time of the lowest point before a rise in GSR)of the respective T2P instances.

After the scores for the T2P instances are determined, the examplemonitoring station 108 uses the scores to assign scores to windows(e.g., time windows, time slots, events, etc.) to generate an intensityprofile. FIG. 5 illustrates an enlarged view of the example score graph406 from 0 seconds to 20 seconds and a corresponding GSR intensityprofile 500 (as discussed in further detail herein).

The window generator 122 defines a plurality of windows. In someexamples, the windows are defined by a fixed time length using a slidingscale. In other words, instead of having fixed consecutive windows, thewindows are defined using a sliding scale. For example, a window T_(W)of fixed time length T_(L) is shifted along the GSR data 402 inincrements of T_(S). For instance or time shift i, the time windowT_(W,i) of interest is defined by time T given in Equation 1.T _(S) ×i≤T≤T _(S) ×i+T _(L)  Equation 1.

The example windows may be defined by any length of time T_(L) and anyincrement T_(S). For example, in the illustrated example of FIG. 5, afixed time length T_(L) of 7 seconds with an increment T_(S) of 1 secondis implemented. Therefore, a first window 502 a occurs or spans fromT=0s to T=7s, a second window 502 b occurs from T=1s to T=8s, a thirdwindow 502 c occurs from T=2s to T=9s, and so forth. Therefore, theexample window generator 122 defines a plurality of windows, where eachof the time windows commences a first time period (e.g., incrementT_(S)) after a preceding time window and each of the time windows has aduration of a second time period (e.g., fixed time length T_(L)), andthe second time period is greater than the first time period. Forexample, the fixed time length T_(L) of 7s is greater than the incrementT_(S) of 1s. As such, the time windows overlap. For example, in theillustrated example of FIG. 5, the first window 502 a partially overlapsin time with the second window 502 b (e.g., from T=1s to T=7s).

After the windows are defined, the window scorer 124 assigns scores tothe respective windows, and the intensity profile generator 126generates the GSR intensity profile 500 as illustrated in a windowedscore graph 504 of FIG. 5. The Y-axis of the windowed score graph 504represents the scores of the windows and the X-axis of the windowedscore graph 504 represents time in seconds.

For example, in this first window 502 a, the highest T2P score is of thefirst T2P instance 404 and has a score of 1. As such, the window scorer124 assigns the first window 502 a a score of 1, as illustrated in theGSR intensity profile 500. In the illustrated example, the score of thefirst window 502 a is associated with the time corresponding to thebeginning of the first window 502 a (i.e., 0s). In the second window 502b, the highest T2P score is of the first T2P instance 404 and has ascore of 1. As such, the window scorer 124 assigns the second window 502b a score of 1, as illustrated in the GSR intensity profile 500. In theillustrated example, the score of the second window 502 b is associatedwith the time corresponding to the beginning of the second window 502 b(i.e., 1s). In the third window 502 c, the highest T2P score is of thefirst T2P instance 404 and has a score of 1. As such, window scorer 124assigns the third window 502 c a score of 1, as illustrated in the GSRintensity profile 500. In the illustrated example, the score of thethird window 502 c is associated with the time corresponding to thebeginning of the third window 502 c (i.e., 2s). The window scorer 124continues to assign scores to each of the windows of based on thehighest scores of the T2P instance(s) occurring in the respectivewindows. In some examples, the window scorer 124 compares the scores ofthe T2P instance(s) to determine the highest scores in each of thewindows and selects the highest scores occurring within the respectivewindows.

In the illustrated example, an eleventh window 502 k occurs at T=11s toT=18s. In this window, the highest T2P score is of a T2P having a scoreof 2. As such, the eleventh window 502 k is assigned a score of 2, asillustrated in the GSR intensity profile 500. In the illustratedexample, the score of the eleventh window 502 k is associated with thetime corresponding to the beginning of the eleventh window 502 k (i.e.,11s). In some examples, if a window includes multiple scores of multipleT2P instances, the highest score occurring in the window is assigned tothe window. In other examples, other ones of the scores may be assignedto the windows (e.g., based on the lowest T2P score within the window,based on an average of all the T2P scores within the window, based on asum of all the T2P scores within the window, etc.). The intensityprofile generator 126 uses the window scores to create the GSR intensityprofile 500. The GSR intensity profile 500 may be displayed on themonitor 110, for example.

Therefore, the GSR intensity profile 500 is generated by determiningscores every time increment T_(S), where the scores are based on windowsof fixed time length T_(L) starting at T_(S,I) and spanning the fixedtime length T_(L). For example, at T=0, a score is generated based on awindow defined by T=0 to T=7, at T=1, a score is generated based on awindow defined by T=1 to T=8, and so forth. This example techniqueemploys overlapping windows of time, which are based on a sliding scalerather than having consecutive time windows. By overlapping the windows(instead of having consecutive windows), a profile is generated that hasa higher resolution (e.g., more granular) and, thus, more accuratelyportraying the intensity of the biological response corresponding to thepresentation. In other words, the scores of the profile are based onupcoming T2P instances (which are scored at the troughs), and the timeafter the troughs of the T2P instances are not assigned the score forthe T2P instance. The GSR intensity profile 500 emphasizes or moreaccurately reflects the time portions before a stimulating event or timein the presentation. As such, the producer(s) of the presentation canmore easily and effectively analyze the results of the presentation.

In the illustrated example, the scores of the respective T2P instancesare based on the statistical boundaries defined by the baseline test, asillustrated in FIGS. 2 and 3. However, in other examples, score valuesmay be determined in other manners. For example, a histogram of the T2Pinstances of the GSR data 402 may be used to determine score values. Inother examples, the score values may be set or provided by a third partyentity. In other examples, the score values may be pre-established basedon multiple baseline tests from one or more subjects.

In some examples, after the GSR intensity profile 500 is generated, theintensity profile generator 126 may normalize or rescale the GSRintensity profile 500 (e.g., from 0 to 1). FIG. 6 illustrates an examplenormalized GSR intensity profile 600 in an example graph 602. In someexamples, multiple GSR intensity profiles from multiple subjects may beaveraged to produce an aggregated GSR intensity profile 700, asillustrated in the example graph 702 of FIG. 7. For example, the scoresof each members GSR intensity profile at every T_(S) may be averaged toproduce the aggregated GSR intensity profile 700. In some examples, eachsubject's GSR intensity profile is normalized first, and then GSRintensity profiles are averaged. In other examples, the GSR intensityprofiles are averaged first, and then the aggregated GSR intensityprofile is normalized (e.g., by maximum score value) to yield a resultfrom 0-1 range, 0 being no response activity (e.g., low intensity) and 1being maximum response activity (e.g., high intensity).

In some examples, the monitoring station 108 can generate a report ofthe GSR intensity profile 500 (FIG. 5) for one or more subjects, thenormalized GSR intensity profile (FIG. 6) for one or more subjects,and/or the aggregated GSR intensity profile 700 (FIG. 7). The report mayidentify the times in the presentation that correlate to increasedand/or decreased GSR intensity and, thus, are indicative of increased ordecreased arousal to the presentation.

In some examples, the aggregated GSR intensity profile 700 may becombined (e.g., aggregated) with biometric response data from one ormore other biometric response measurement device(s) 132 (FIG. 1). Thebiometric response data may include, for example, heart rate, heart ratevariability, vagal tone, respiration data, body movement data, measuresof facial muscle movement/expression, body temperature data, near bodytemperature data, facial and body thermography imaging, EEG, EKG, facialEMG, fMRI, eye movement, etc. The measurement device(s) 132 may be, forexample, an eye tracking device (e.g., a camera) for monitoring eyefixation location and/or fixation duration, one or more EEG electrodescoupled to the head of the subject 102 for obtaining EEG signals, one ormore EKG electrodes coupled to the subject 102 for obtaining EKGsignals, etc. In some examples, the example monitoring station 108obtains the biometric response data and combines it with the aggregatedGSR intensity profile 700 to generate an engagement profile. Theengagement profile may be used to identify elements in the presentationthat correlate to increased and/or decreased engagement with thepresentation. In some examples, the aggregated GSR intensity profile 700and/or the engagement profile may be used to determine the success ofone or more portions (e.g., scenes, events, etc.) of the presentationand/or the overall presentation, to determine the effectiveness of oneor more portions of the presentation and/or the overall presentation,etc.

For example, the effectiveness determiner 128 may determine aneffectiveness of the presentation based on the aggregated GSR intensityprofile 700 (e.g., based on the scores of the windows 502 a-502 n) bycomparing a number of times the score of the aggregated GSR intensityprofile 700 meets (e.g., exceeds) an intensity threshold within a periodof time (e.g., 50 seconds). Additionally or alternatively, theeffectiveness may be based on a duration of the instance(s) of the GSRintensity meets the intensity threshold. In another example, theeffectiveness determiner 128 may determine an effectiveness of a portionor element of the presentation based on the aggregated GSR intensityprofile 700 by comparing the score(s) during the element to an intensitythreshold. For example, if the score(s) during the element (e.g., duringa scene occurring at 10s-20s) of the presentation is above thethreshold, the element may be identified as eliciting high levels ofresponse (e.g., arousal), and if the score(s) during the element of thepresentation are below the threshold, the element may be identified aseliciting low levels of response. In some examples, the effectivenessdeterminer 128 and/or the presentation modifier 130 may identifyelements of the presentation that can be modified to enhance theeffectiveness of the presentation (e.g., by removing or replacing theelements causing low levels of response and/or repeating or highlightingelements that cause high levels of response). For example, thepresentation modifier 130 may automatically select between twoalternative presentation segments (e.g., scenes, events, etc.) based onthe effectiveness of one or both of the presentation segments. In someexamples, the effectiveness determiner 128 and/or the presentationmodifier 130 may identify an element of a presentation corresponding toa level of GSR satisfying a threshold, and the presentation modifier 130may modify the element to no longer satisfy the threshold. For example,the effectiveness determiner 128 and/or the presentation modifier 130may identify that an element of that fails to satisfy or meet athreshold (e.g., music in a scene in a horror film does not cause thedesired biological response indicative of anxiety or fear), and thepresentation modifier 130 modifies the element to cause the desiredresponse (e.g., to cause the desired biological response). For example,the threshold may be a low threshold or high threshold, and if theelement satisfies the low threshold (e.g., falls below the lowthreshold) or the high threshold (e.g., exceeds the high threshold), thepresentation modifier 130 modifies the element to no longer satisfy thethreshold (e.g., by replacing the element with an alternate element thatcauses increased GSR response that brings the response above the lowthreshold, or by replacing the element with an alternate element thatcauses decreased GSR response that brings the response below the highthreshold). In some examples, the effectiveness determiner 128 and/orthe presentation modifier 130 may configure the output of a device suchas a cell phone color scheme in response to the effectiveness.

In some examples, the aggregated GSR intensity profile 700 and/or theengagement profile for a presentation may be ranked among other GSRintensity profiles and/or engagement profiles for other presentations(e.g., stored in the database 114). In some examples, the effectivenessdeterminer 128 may determine an effectiveness of the presentation basedon the ranking of the aggregated GSR intensity profile 700 among theother GSR intensity profiles.

While an example manner of implementing the system 100 of FIG. 1 isillustrated in FIG. 1, one or more of the elements, processes and/ordevices illustrated in FIG. 1 may be combined, divided, re-arranged,omitted, eliminated and/or implemented in any other way. Further, theexample monitoring station 108, the example database 114, the exampleT2P identifier 116, the example T2P value determiner 118, the examplescore value determiner 119, the example T2P scorer 120, the examplewindow generator 122, the example window scorer 124, the exampleintensity profile generator 126, the example effectiveness determiner128, the example presentation modifier 130 and/or, more generally, theexample system 100 of FIG. 1 may be implemented by hardware, software,firmware and/or any combination of hardware, software and/or firmware.Thus, for example, any of the example monitoring station 108, theexample database 114, the example T2P identifier 116, the example T2Pvalue determiner 118, the example score value determiner 119, theexample T2P scorer 120, the example window generator 122, the examplewindow scorer 124, the example intensity profile generator 126, theexample effectiveness determiner 128, the example presentation modifier130 and/or, more generally, the example system 100 could be implementedby one or more analog or digital circuit(s), logic circuits,programmable processor(s), application specific integrated circuit(s)(ASIC(s)), programmable logic device(s) (PLD(s)) and/or fieldprogrammable logic device(s) (FPLD(s)). When reading any of theapparatus or system claims of this patent to cover a purely softwareand/or firmware implementation, at least one of the example monitoringstation 108, the example database 114, the example T2P identifier 116,the example T2P value determiner 118, the example score value determiner119, the example T2P scorer 120, the example window generator 122, theexample window scorer 124, the example intensity profile generator 126,the example effectiveness determiner 128, and/or the examplepresentation modifier 130 is/are hereby expressly defined to include atangible computer readable storage device or storage disk such as amemory, a digital versatile disk (DVD), a compact disk (CD), a Blu-raydisk, etc. storing the software and/or firmware. Further still, theexample system 100 of FIG. 1 may include one or more elements, processesand/or devices in addition to, or instead of, those illustrated in FIG.1, and/or may include more than one of any or all of the illustratedelements, processes and devices.

A flowchart representative of example machine readable instructions forimplementing the system 100 of FIG. 1 is shown in FIGS. 8A and 8B. Inthis example, the machine readable instructions comprise a program forexecution by a processor such as the processor 912 shown in the exampleprocessor platform 900 discussed below in connection with FIG. 9. Theprogram may be embodied in software stored on a tangible computerreadable storage medium such as a CD-ROM, a floppy disk, a hard drive, adigital versatile disk (DVD), a Blu-ray disk, or a memory associatedwith the processor 912, but the entire program and/or parts thereofcould alternatively be executed by a device other than the processor 912and/or embodied in firmware or dedicated hardware. Further, although theexample program is described with reference to the flowchart illustratedin FIGS. 8A and 8B, many other methods of implementing the examplesystem 100 may alternatively be used. For example, the order ofexecution of the blocks may be changed, and/or some of the blocksdescribed may be changed, eliminated, or combined.

As mentioned above, the example process of FIGS. 8A and 8B may beimplemented using coded instructions (e.g., computer and/or machinereadable instructions) stored on a tangible computer readable storagemedium such as a hard disk drive, a flash memory, a read-only memory(ROM), a compact disk (CD), a digital versatile disk (DVD), a cache, arandom-access memory (RAM) and/or any other storage device or storagedisk in which information is stored for any duration (e.g., for extendedtime periods, permanently, for brief instances, for temporarilybuffering, and/or for caching of the information). As used herein, theterm tangible computer readable storage medium is expressly defined toinclude any type of computer readable storage device and/or storage diskand to exclude propagating signals and to exclude transmission media. Asused herein, “tangible computer readable storage medium” and “tangiblemachine readable storage medium” are used interchangeably. Additionallyor alternatively, the example process of FIGS. 8A and 8B may beimplemented using coded instructions (e.g., computer and/or machinereadable instructions) stored on a non-transitory computer and/ormachine readable medium such as a hard disk drive, a flash memory, aread-only memory, a compact disk, a digital versatile disk, a cache, arandom-access memory and/or any other storage device or storage disk inwhich information is stored for any duration (e.g., for extended timeperiods, permanently, for brief instances, for temporarily buffering,and/or for caching of the information). As used herein, the termnon-transitory computer readable medium is expressly defined to includeany type of computer readable storage device and/or storage disk and toexclude propagating signals and to exclude transmission media. As usedherein, when the phrase “at least” is used as the transition term in apreamble of a claim, it is open-ended in the same manner as the term“comprising” is open ended.

FIGS. 8A and 8B are a flowchart representative of example machinereadable instructions 800 that may be executed to generate a GSRintensity profile for a presentation. While the instructions 800 ofFIGS. 8A and 8B are described in connection GSR data, the exampleinstructions 800 can likewise be applied to other biological responses(e.g., heart rate, respiration rate, eye tracking, EEG, etc.) inaddition to or as an alternative to GSR data.

In some examples, prior to analyzing a subject's GSR, baseline data isobtained. In some examples, the baseline data is determined based on abaseline test, which may be a test or sample presentation presented tothe subject (e.g., before or after the obtaining the subject's GSR to atarget presentation). Blocks 802-808 represent example instructions forobtaining and analyzing baseline data to create score values. At block802, the monitoring station 108 obtains baseline GSR data, such as theGSR data 202 illustrated in FIG. 2. The baseline GSR data is obtainedwhile the subject 102 is exposed to a test or sample presentation (e.g.,via the presentation device 106). At block 804, the T2P identifier 116identifies the T2P instances in the baseline GSR data. The T2P valuedeterminer 118 determines the values of the GSR levels of the T2Pinstances at block 806. At block 808, the score value determiner 119divides the T2P values into percentiles and assigns score values to thepercentiles. In some examples, the percentiles and the score values arestored in the database 114. In some examples, the percentiles representranges of GSR levels. The score value determiner 119 may divide the T2Pvalues into any arrangement. For example, as illustrated in thehistogram 300 of FIG. 3, score value determiner 119 divides the T2Pinstances into quartiles, each representing a range of GSR values. Inother examples, the score values may be specified for a user ordetermined in another manner. In such an instance, blocks 802-808 maynot be performed. Instead, the example instructions 800 may beginexecuting at block 810.

At block 810, the monitoring station 108 obtains GSR data (e.g., the GSRdata 402) from the subject 102 while the subject 102 is exposed to atarget or desired presentation. The presentation may be presented by anypresentation device, such as a TV, a speaker, etc. At block 812, the T2Pidentifier 116 identifies the T2P instances in the GSR data. In someexamples, the obtaining of the GSR data (block 810) and the identifyingof the T2P instances (block 812) may be performed by one entity, and oneor more of the subsequent analyzing processes may be performed byanother (separate) entity. In some examples, a first entity may gatherthe GSR data (block 810) and identify the T2P instances (bock 812)(including the troughs and peaks) and a second entity may analyze theinformation and generate the GSR intensity profile.

At block 814, the T2P value determiner 118 determines or measures thevalues or levels of rise in the respective T2P instances. At block 816,the T2P scorer 120 assigns scores (e.g., trough-to-peak scores) to eachof the T2P instances. In some examples, the T2P scorer 120 assigns thescores to the troughs of the respective T2P instances. In some examples,the scores are determined based on the score values from the baselinetest. For example, as illustrated in FIG. 4, the first T2P instance 404has a GSR value or level of 0.05 which corresponds to Quartile 1 of thescore values. As such, the first T2P instance 404 is assigned a scoreof 1. In some examples, the scores of the respective T2P instances areassigned or associated with the times of the troughs of the respectiveT2P instances. For example, as illustrated in FIG. 4, the score of 1 forthe first T2P instance 404 is associated with the time at which thetrough occurs. In other examples, the scores for the T2P instances maybe associated with other parts of the T2P instances (e.g., the peaks,the time of half of the total rise, etc.). In some examples, the scoresare plotted or graphed in the score graph 406.

At block 818, the window generator 122 generates or defines a pluralityof windows. In some examples, the windows have fixed lengths and arebased on a sliding scale. In other words, each of the time windowscommences a first time period after a preceding time window and each ofthe time windows having a duration of a second time period, where thesecond time period greater than the first time period. Equation 1 aboveillustrates an example equation for defining a plurality of windowshaving a fixed length based on a sliding scale. For example, asillustrated in FIG. 5, the first window 502 a occurs or spans from T=0sto T=7s, the second window 502 b occurs from T=1s to T=8s, the thirdwindow 502 c occurs from T=2s to T=9s, and so forth. In other words,every increment (T_(S)) a window is defined having a time length(T_(L)). In the illustrated example of FIG. 5, each of the windows 502a-502 n has the same length (T_(L)) (e.g., 7s) using a sliding scale orincrement (T_(S)) (e.g., 1s).

At block 820, the window scorer 124 assigns scores (e.g., window scores)to the windows based on the scores of the T2P instance(s) within therespective windows. For example, as illustrated in the score graph 406in FIG. 5, the first window 502 a includes the score of 1 correspondingto the first T2P instance 404. As such, the window scorer 124 assigns ascore of 1 to the first window 502 a. This process continues for each ofthe windows. In some examples, multiple scores for multiple T2Pinstances are included in a single window. In some examples, the highestscore of all of the T2P instances in the window is assigned to therespective window. In some examples, the window scorer 124 assigns thescores to the start times of the respective windows.

Execution of example instructions 800 continues in FIG. 8B. At block822, the intensity profile generator 126 generates or creates anintensity profile for the presentation based on the scores of thewindows. For example, as illustrated in FIG. 5, the intensity profile500 is generated based on the scores of the windows. In the illustratedexample, the scores of the windows are assigned or associated with thetimes of the beginnings of the respective windows. In other examples,the scores for the windows may be assigned to other points in timeoccurring (or not occurring) within the respective windows.

In some examples, the GSR intensity profile is normalized or scaled(e.g., from 0-1) at block 824. For example, FIG. 6 illustrates theexample graph 602 for the normalized GSR intensity profile 600 of thesubject 102 to the presentation. In some examples, the normalized orscaled GSR intensity profile for a subject is averaged with othernormalized GSR intensity profiles from one or more other subjects atblock 826 to generate the aggregated GSR intensity profile 700. In otherexamples, the GSR intensity profiles of multiple subjects are averagedfirst, and then the averaged or aggregated GSR intensity profile isnormalized.

The GSR intensity profile 500 for a subject (FIG. 5), the normalized GSRintensity profile 600 for a subject (FIG. 6), and/or the aggregated GSRintensity profile 700 (FIG. 7) may be used to identify elements (e.g.,scenes, events, etc.) of the presentation that cause the highest andlowest levels of GSR intensity. The GSR intensity profile 500, asillustrated in the normalized example of FIG. 6, more clearly identifiesthe areas of the presentation that elicit the strongest responses, ascompared to the raw GSR data 402. The GSR intensity profile 500 (FIG.5), the normalized GSR intensity profile 600 (FIG. 6), and/or theaggregated GSR intensity profile 700 (FIG. 7) may be displayed (e.g.,via the monitor 110) and/or generated into a report that is output bythe system 100.

In some examples, the GSR intensity profile 500 of one or more subjects(or the aggregated GSR intensity profile 700 (block 826)) may becombined with one or more biometric response measurements to produce anengagement profile (block 828). Additional biometric responsemeasurements may include, for example, heart rate, brain wave data,respiratory response data, body movement data, eye tracking data, facialexpression data (e.g., facial emotion encoding), etc. For example, themonitoring station 108 may combine the aggregated GSR intensity profile700 with data obtained from an eye tracking sensor (e.g., themeasurement device(s) 132 of FIG. 1) to determine where the subject 102is gazing or focused during the presentation when the subject 102 isaroused (e.g., based on the GSR intensity profile). In some examples,the monitoring station 108 may generate a report including the GSRintensity profile 500 (e.g., for a subject), the normalized GSRintensity profile 600 (e.g., for the subject), the aggregated GSRintensity profile 700 and/or the engagement profile (block 830). Theaggregated GSR intensity profile 700 and/or the engagement profile maybe used to determine the success of one or more portions (e.g., scenes,events, etc.) of the presentation and/or the overall presentation, todetermine the effectiveness of one or more portions of the presentationand/or the overall presentation, etc. In some examples, the aggregatedGSR intensity profile 700 and/or engagement profile may be used todetermine which elements of the presentation elicited or caused highintensity and/or engagement and/or low intensity and/or engagement and,thus, may be modified to increase or decrease the engagement based onthe desired outcome. In some examples, the engagement profile isdisplayed on the monitor 110. Additionally or alternatively, theengagement profile may be printed and distributed, for example. Also, insome examples, the aggregated GSR intensity profile 700 and/or theengagement profile may be used to better predict audience responses toanother presentation (e.g., a second or subsequent presentation).

In some examples, the monitoring station 108 identifies one or moreelements (e.g., a scene, an event, etc.) in the presentationcorresponding to high and/or low intensity and/or engagement based onthe aggregated GSR intensity profile 700 and/or the engagement profile(block 832). In some examples, at block 834, the example instructions800 determine whether one or more elements of the presentation are to bealtered or modified based on the identification of the high and/or lowintensity and/or engagement element(s). If not, execution of the exampleinstructions 800 may end (block 836). However, if it is determined thatone or more elements of the presentation are to be altered or modified(block 834), execution of the example instructions 800 continues andmodifies or alters the one or more element(s) (block 838). For example,the effectiveness determiner 128 and/or the presentation modifier 130may identify an element of the presentation corresponding to a low levelof GSR intensity and the presentation modifier 130 may remove or replacethe element with another element (e.g., an alternate ending, event,etc.).

After modification of the element(s) (block 838), the exampleinstructions 800 determine whether or not subsequent monitoring of thesubject's response is to continue (block 840). If not, execution of theexample instructions 800 may end (block 836). However, if monitoring ofa subject's response is to continue (block 840), the control returns toblock 810 (FIG. 8A) where obtains GSR data is obtained from the subjectduring the subsequent or continued monitoring, and execution of theexample instructions 800 continues as disclosed above.

FIG. 9 is a block diagram of an example processor platform 900structured to execute the instructions of FIGS. 8A and 8B to implementthe example monitoring station 108, the example database 114, theexample T2P identifier 116, the example T2P value determiner 118, theexample score value determiner 119, the example T2P scorer 120, theexample window generator 122, the example window scorer 124, the exampleintensity profile generator 126, the example effectiveness determine 128and/or the example presentation modifier 130 of FIG. 1. By way ofexample, FIG. 9 shows the example monitoring station 108, the exampledatabase 114, the example T2P identifier 116, the example T2P valuedeterminer 118, the example score value determiner 119, the example T2Pscorer 120, the example window generator 122, the example window scorer124, the example intensity profile generator 126, the exampleeffectiveness determine 128 and the example presentation modifier 130,but the processor platform 900 may include more of, fewer of, ordifferent ones of the example monitoring station 108, the exampledatabase 114, the example T2P identifier 116, the example T2P valuedeterminer 118, the example score value determine 119, the example T2Pscorer 120, the example window generator 122, the example window scorer124, the example intensity profile generator 126, the exampleeffectiveness determiner 128 and/or the example presentation modifier130 of FIG. 1. The processor platform 900 can be, for example, a server,a personal computer, a mobile device (e.g., a cell phone, a smart phone,a tablet such as an iPad™), a personal digital assistant (PDA), anInternet appliance, a DVD player, a CD player, a digital video recorder,a Blu-ray player, a gaming console, a personal video recorder, a set topbox, or any other type of computing device.

The processor platform 900 of the illustrated example includes aprocessor 912. The processor 912 of the illustrated example is hardware.For example, the processor 912 can be implemented by one or moreintegrated circuits, logic circuits, microprocessors or controllers fromany desired family or manufacturer.

The processor 912 of the illustrated example includes a local memory 913(e.g., a cache). The processor 912 of the illustrated example is incommunication with a main memory including a volatile memory 914 and anon-volatile memory 916 via a bus 918. The volatile memory 914 may beimplemented by Synchronous Dynamic Random Access Memory (SDRAM), DynamicRandom Access Memory (DRAM), RAMBUS Dynamic Random Access Memory (RDRAM)and/or any other type of random access memory device. The non-volatilememory 916 may be implemented by flash memory and/or any other desiredtype of memory device. Access to the main memory 914, 916 is controlledby a memory controller.

The processor platform 900 of the illustrated example also includes aninterface circuit 920. The interface circuit 920 may be implemented byany type of interface standard, such as an Ethernet interface, auniversal serial bus (USB), and/or a PCI express interface.

In the illustrated example, one or more input devices 922 are connectedto the interface circuit 920. The input device(s) 922 permit(s) a userto enter data and commands into the processor 912. The input device(s)can be implemented by, for example, an audio sensor, a microphone, acamera (still or video), a keyboard, a button, a mouse, a touchscreen, atrack-pad, a trackball, isopoint and/or a voice recognition system.

One or more output devices 924 are also connected to the interfacecircuit 920 of the illustrated example. The output devices 924 can beimplemented, for example, by display devices (e.g., a light emittingdiode (LED), an organic light emitting diode (OLED), a liquid crystaldisplay, a cathode ray tube display (CRT), a touchscreen, a tactileoutput device, a printer and/or speakers). The interface circuit 920 ofthe illustrated example, thus, typically includes a graphics drivercard, a graphics driver chip or a graphics driver processor.

The interface circuit 920 of the illustrated example also includes acommunication device such as a transmitter, a receiver, a transceiver, amodem and/or network interface card to facilitate exchange of data withexternal machines (e.g., computing devices of any kind) via a network926 (e.g., an Ethernet connection, a digital subscriber line (DSL), atelephone line, coaxial cable, a cellular telephone system, etc.).

The processor platform 900 of the illustrated example also includes oneor more mass storage devices 928 for storing software and/or data.Examples of such mass storage devices 928 include floppy disk drives,hard drive disks, compact disk drives, Blu-ray disk drives, RAIDsystems, and digital versatile disk (DVD) drives.

The coded instructions 932 of FIGS. 8A and 8B may be stored in the massstorage device 928, in the volatile memory 914, in the non-volatilememory 916, and/or on a removable tangible computer readable storagemedium such as a CD or DVD.

From the foregoing, it will be appreciated that methods,apparatus/systems and articles of manufacture have been disclosed thatdetermine an accurate intensity measurement of a subject's biologicalresponse, such as GSR, to a presentation. The intensity measurements maybe used to identify elements (e.g., scenes, events, etc.) in thepresentation that correspond to high and/or low levels of response fromthe subject. The intensity measurements may therefore be used to betterpredict how a similar presentation may affect an audience, for example.

Although certain example methods, apparatus and articles of manufacturehave been disclosed herein, the scope of coverage of this patent is notlimited thereto. On the contrary, this patent covers all methods,apparatus and articles of manufacture fairly falling within the scope ofthe claims of this patent.

What is claimed is:
 1. A system to modify media content based onphysiological changes to a subject's skin, the system comprising: agalvanic skin response (GSR) sensor to gather GSR data from the subjectduring a period of time while the subject is exposed to the mediacontent; and a processor communicatively coupled to the GSR sensor, theprocessor to: define a plurality of time windows in the period of time,the plurality of time windows including: a first time window having aduration of a first time period, a second time window having a durationof the first time period, the second time window commencing a secondtime period after the first time window commences, the first time periodgreater than the second time period, the second time window commencingprior to an end of the first time window, and the first and second timewindows partially overlap, and a third time window having a duration ofthe first time period, the third time window commencing the second timeperiod after the second time window commences, the third time windowcommencing prior to an end of the second time window, the second andthird time windows partially overlap, and the time windows havingrespective start times and ends times; determine a first score based onone or more spikes in the GSR data occurring during the first timewindow, the first score different than a value associated with the GSRdata at a start time of the first time window; determine a second scorebased one or more spikes in the GSR data occurring during the secondtime window; determine a third score based one or more spikes in the GSRdata occurring during the third time window; generate a GSR intensityprofile, the GSR intensity profile including: the first score assignedto a time in the GSR intensity profile that corresponds to the starttime of the first time window; the second score assigned to a time inthe GSR intensity profile that corresponds to a start time of the secondtime window; and the third score assigned to a time in the GSR intensityprofile that corresponds to a start time of the third time window;identify, based on the GSR intensity profile, an element of the mediacontent corresponding to a level of GSR satisfying a threshold; andmodify the element of the media content to create a modified mediacontent.
 2. The system of claim 1, wherein the processor is to determinethe first, second, and third scores based on a highest spike occurringwithin the corresponding time windows.
 3. The system of claim 1, whereinthe subject is a first subject, and the processor is to: average the GSRintensity profile of the first subject with a GSR intensity profile of asecond subject to create an aggregated GSR intensity profile; normalizethe aggregated GSR intensity profile to create a normalized aggregatedGSR intensity profile; and identify the element of the media contentbased on the normalized aggregated GSR intensity profile.
 4. The systemof claim 1, wherein the processor is to: identify spikes in baseline GSRdata obtained from the subject during a baseline test; divide the spikesfrom the baseline test into ranges; and respectively assign baselinevalues to the ranges, the first, second, and third scores based thebaseline values.
 5. The system of claim 1, wherein the GSR intensityprofile includes a graph of the first, second, and third scores plottedat the respective assigned times.
 6. The system of claim 1, wherein theprocessor is to modify the element by at least one of removing theelement, repeating the element, or replacing the element with analternative element.
 7. The system of claim 1, wherein the first, secondand third scores are window scores, wherein the processor is to assigntrough-to-peak scores to trough-to-peak instances occurring in the GSRdata, and wherein each of the window scores corresponds to a highesttrough-to-peak score of a trough-to-peak instance of a plurality of thetrough-to-peak instances occurring within the respective first, second,or third time window.
 8. A non-transitory machine readable storagemedium comprising instructions that, when executed, cause a machine toat least: assign trough-to-peak scores to trough-to-peak instancesoccurring in galvanic skin response (GSR) data obtained from a subjectexposed to media content during a period of time; define a plurality oftime windows in the period of time during which the subject is exposedto the media content, the plurality of time windows including: a firsttime window having a duration of a first time period, a second timewindow having a duration of the first time period, the second timewindow commencing a second time period after the first time windowcommences, the first time period greater than the second time period,such that the second time window commences prior to an end of the firsttime window, and such that the first and second time windows partiallyoverlap, and a third time window having a duration of the first timeperiod, the third time window commencing the second time period afterthe second time window commences, such that the third time windowcommences prior to an end of the second time window, and such that thesecond and third time windows partially overlap; determine a firstwindow score based on a highest trough-to-peak score of a trough-to-peakinstance of a plurality of the trough-to-peak instances occurring withinthe first time window, the first window score different than a valueassociated with the GSR data at a start time of the first time window;determine a second window score based on a highest trough-to-peak scoreof a trough-to-peak instance of a plurality of the trough-to-peakinstances occurring within the second time window; determine a thirdwindow score based on a highest trough-to-peak score of a trough-to-peakinstance of a plurality of the trough-to-peak instances occurring withinthe third time window; generate a GSR intensity profile, the GSRintensity profile including: the first score assigned to a time in theGSR intensity profile that corresponds to the start time of the firsttime window; the second score assigned to a time in the GSR intensityprofile that corresponds to a start time of the second time window; andthe third score assigned to a time in the GSR intensity profile thatcorresponds to a start time of the third time window; identify, based onthe GSR intensity profile, an element of the media content correspondingto a level of GSR satisfying a threshold; and modify the identifiedelement of the media content to create a modified media content.
 9. Thenon-transitory machine readable storage medium of claim 8, wherein thesubject is a first subject, and wherein the instructions, when executed,further cause the machine to: average the GSR intensity profile of thefirst subject with a GSR intensity profile of a second subject to createan aggregated GSR intensity profile; normalize the aggregated GSRintensity profile to create a normalized aggregated GSR intensityprofile; and identify the element based on the normalized aggregated GSRintensity profile.
 10. The non-transitory machine readable storagemedium of claim 8, wherein the instructions, when executed, cause themachine to: identify spikes in baseline GSR data obtained from thesubject during a baseline test; divide the spikes from the baseline testinto ranges; and respectively assign baseline values to the ranges, thetrough-to-peak scores based on the baseline values.
 11. Thenon-transitory machine readable storage medium of claim 8, wherein theGSR intensity profile is a graph of the first, second, and third windowscores plotted at the respective assigned times.
 12. The non-transitorymachine readable storage medium of claim 8, wherein the instructions,when executed, cause the machine to modify the element by at least oneof removing the element, repeating the element, or replacing the elementwith an alternative element.
 13. The non-transitory machine readablestorage medium of claim 8, wherein the instructions, when executed,cause the machine to assign the trough-to-peak scores to thetrough-to-peak instances at times corresponding to troughs ofcorresponding ones of the trough-to-peak instances.
 14. A system tomodify media content based on physiological changes to a subject's skin,the system comprising: means for gathering galvanic skin response (GSR)data from the subject during a period of time while the subject isexposed to the media content; means for defining a plurality of timewindows in the period of time, the plurality of time windows including:a first time window having a duration of a first time period, a secondtime window having a duration of the first time period, the second timewindow commencing a second time period after the first time windowcommences, the first time period greater than the second time period,such that the second time window commences prior to an end of the firsttime window, and such that the first and second time windows partiallyoverlap, and a third time window having a duration of the first timeperiod, the third time window commencing the second time period afterthe second time window commences, such that the third time windowcommences prior to an end of the second time window, and such that thesecond and third time windows partially overlap; means for assigningscores to corresponding ones of the first, second, and third timewindows based on spikes in the GSR data occurring within thecorresponding first, second, and third time windows, the means forassigning to: determine a first score based on one or more of the spikesin the GSR data occurring during the first time window, the first scoredifferent than a value associated with the GSR data at a start time ofthe first time window; determine a second score based on one or more ofthe spikes in the GSR data occurring during the second time window; anddetermine a third score based on one or more of the spikes in the GSRdata occurring during the third time window; means for generating a GSRintensity profile, the GSR intensity profile being a graph of the scoresover time, the GSR intensity profile including: the first score assignedto a time in the GSR intensity profile that corresponds to the starttime of the first time window; the second score assigned to a time inthe GSR intensity profile that corresponds to a start time of the secondtime window; and the third score assigned to a time in the GSR intensityprofile that corresponds to a start time of the third time window; meansfor identifying, based on the GSR intensity profile, an element of themedia content corresponding to a level of GSR satisfying a threshold;and means for modifying the identified element of the media content tocreate a modified media content.
 15. The system of claim 14, wherein theassigning means is to determine the first, second, and third scoresbased on a highest spike occurring within the corresponding first,second, and third time windows.
 16. The system of claim 14, wherein thesubject is a first subject, further including: means for averaging theGSR intensity profile of the first subject with a GSR intensity profileof a second subject to create an aggregated GSR intensity profile; andmeans for normalizing the aggregated GSR intensity profile to create anormalized aggregated GSR intensity profile, wherein the identifyingmeans is to identify the element based on the normalized aggregated GSRintensity profile.
 17. The system of claim 14, further including: meansfor identifying spikes in baseline GSR data obtained from the subjectduring a baseline test; and means for dividing the spikes from thebaseline test into ranges and assigning baseline values to the ranges,the first, second, and third scores based the baseline values.
 18. Thesystem of claim 14, wherein the modifying means is to modify the elementby at least one of removing the element, repeating the element, orreplacing the element with an alternative element.
 19. An apparatuscomprising: at least one memory; instructions in the apparatus; andprocessor circuitry to execute the instructions to: define a pluralityof time windows in a period of time during which a subject is exposed tomedia content, the plurality of time windows including: a first timewindow having a duration of a first time period, a second time windowhaving a duration of the first time period, the second time windowcommencing a second time period after the first time window commences,the first time period greater than the second time period, the secondtime window commencing prior to an end of the first time window, and thefirst and second time windows partially overlap, and a third time windowhaving a duration of the first time period, the third time windowcommencing the second time period after the second time windowcommences, the third time window commencing prior to an end of thesecond time window, the second and third time windows partially overlap,and the time windows having respective start times and ends times;determine a first score based on one or more spikes in galvanic skinresponse (GSR) data occurring during the first time window, the GSR dataobtained from the subject during the period of time, the first scoredifferent than a value associated with the GSR data at a start time ofthe first time window; determine a second score based one or more spikesin the GSR data occurring during the second time window; determine athird score based one or more spikes in the GSR data occurring duringthe third time window; generate a GSR intensity profile, the GSRintensity profile including: the first score assigned to a time in theGSR intensity profile that corresponds to the start time of the firsttime window; the second score assigned to a time in the GSR intensityprofile that corresponds to a start time of the second time window; andthe third score assigned to a time in the GSR intensity profile thatcorresponds to a start time of the third time window; identify, based onthe GSR intensity profile, an element of the media content correspondingto a level of GSR satisfying a threshold; and modify the element of themedia content to create a modified media content.
 20. The apparatus ofclaim 19, wherein the processor circuitry is to determine the first,second, and third scores based on a highest spike occurring within thecorresponding time windows.
 21. The apparatus of claim 19, wherein thesubject is a first subject, and the processor circuitry is to: averagethe GSR intensity profile of the first subject with a GSR intensityprofile of a second subject to create an aggregated GSR intensityprofile; normalize the aggregated GSR intensity profile to create anormalized aggregated GSR intensity profile; and identify the element ofthe media content based on the normalized aggregated GSR intensityprofile.
 22. The apparatus of claim 19, wherein the processor circuitryis to: identify spikes in baseline GSR data obtained from the subjectduring a baseline test; divide the spikes from the baseline test intoranges; and respectively assign baseline values to the ranges, thefirst, second, and third scores based the baseline values.
 23. Theapparatus of claim 19, wherein the GSR intensity profile includes agraph of the first, second, and third scores plotted at the respectiveassigned times.
 24. The apparatus of claim 19, wherein the processorcircuitry is to modify the element by at least one of removing theelement, repeating the element, or replacing the element with analternative element.
 25. The apparatus of claim 19, wherein the first,second and third scores are window scores, wherein the processorcircuitry is to assign trough-to-peak scores to trough-to-peak instancesoccurring in the GSR data, and wherein each of the window scorescorresponds to a highest trough-to-peak score of a trough-to-peakinstance of a plurality of the trough-to-peak instances occurring withinthe respective first, second, or third time window.